Data Visualization Best Practices for Travel Organizations

Plenty of people believe that data visualization is simply making numbers look nice, but data visualization serves several important purposes—and it can be supremely helpful once you’ve grasped its place in the big picture of your business.

One reason data visualization works so well for humans is that we process visual information much more quickly and efficiently through the brain’s visual cortex. It doesn’t require much effort. With numbers, on the other hand, we must use the cerebral cortex, which takes longer and uses more energy. Simply put, it’s just easier for the human mind to make sense of complex information when it is presented visually.

But there’s more. Not all data is what it appears to be. Take Anscombe’s Quartet, for example. Anscombe’s Quarter demonstrates that four different sets of numbers may seem to be very similar, but when presented visually, it becomes clear they are entirely different.

The difference perfectly demonstrates why data visualization works so much better for humans when it comes to understanding complex information. So how can travel companies use data visualization to get better insights and become more innovative? We start with four best practices.

1). Measure your perceptions

Ask the right questions, THEN find the right measures. Our perceptions are frequently off base with reality. For instance, there is a widespread perception in the Western world that Europe is rife with terrorist attacks; however, a look at a world map shows that Europe has relatively few attacks in comparison with many other locations.

Checking in on your perceptions of trends and ROI is essential to making shifts in your approach that actually matter. Create questions around perceptions and then explore the reality. For instance, you might ask:

What is our advance purchase by department?

What is the top true origin and destination?

What is our top property in our top destination?

Have airfare averages increased to the top destination? Is this because of advance purchase trends (something that can be controlled) or external circumstances?

For the purposes of our exploration here, the organization in question believes their air spend is too high, and that they are being too frivolous in booking trips.

2). Question Your KPIs

KPIs are not created equally throughout all data sets. For instance, using averages alone can create problems that really can only be seen through data visualization. Let’s say you’re looking at airfare spend. (We’ll use an outlandish outlier to prove the point here, but you almost certainly have some of these seemingly outlandish numbers hidden around in your data, skewing the results.) You have the following data set:

{254, 300, 325, 380, 465, 480, 540, 100,000}

The average of airfares is: $12,843

The median, however, is: $423

Visualizing both the average and the median would be important. Why? Because a visual of the average show you very clearly that there is an outlier you should account for that would, in fact, lead you to believe the air spend it too high. When you see the outlier, it raises fresh questions about the data?

With the median, on the other hand, you get a real sense of what’s happening with airfare costs, especially if the outlier is accurate. For instance, one or two super costly airfares threw off the budget.

3). Keep it easyWith access to a great deal of data, we tend to complicate things. Keep your data visualization simple by choosing three or four essential trends to review within a category, thereby ensuring you can really see the trends. Again, notice the difference between your ability to make sense of data when it is presented as numbers vs. images.

In this example, we have chosen to look at air spend, the volume of air tickets, and the average of airfare over the same periods of time across three different airlines. The main takeaway: Spending on airfare is down over a two year time period, but it has more to do with a drop in the volume of tickets purchased than it does with airfare. This directly challenges the original perception that the organization is being frivolous with flights. It also raises more valuable questions for an organization. Why has the volume dropped? What is the effect of this drop in volume on the organization (i.e., Is the sales team making fewer visits to clients and, if so, what is the effect on sales volume and revenue)?

4). Provide the answer

Ultimately, the question at hand here is “How much is my air spend?” With the visual representation, you can see clearly what a 22.7% decline in air spend looks like, and it is far more substantial than the number would suggest. Not only is spend not off the charts, it is consistently better than it’s been in the past two years.

Data visualization really is, at its core, about perception versus reality and our enhanced ability to make sense reality when we’re able to process information more effortlessly. Organizations are able to integrate various other data sources together to explore innovative, previously untapped, areas of productivity. For instance, what is each sales person’s ROI on trips? What would a shift in hotel policy look like for revenue and/or team satisfaction? Does having more amenities included in the hotel rate have an impact on overall trip spend (i.e., breakfast, golf, etc.)? The opportunities for exploring are seemingly endless, which is why it is essential to start simple, pay attention to KPIs, and present it simply when representing the data.